z.par2qua {lmomco} | R Documentation

## Quantile Function of the Blipped Distributions

### Description

This function acts as a front end or dispatcher to the distribution-specific quantile functions.

F(x) = begin{cases} 0, & x <= 0 \ p + (1-p)G(x), & x > 0 end{cases}

### Usage

    z.par2qua(f,p,para,z=0,...)

### Arguments

`f` | Nonexceedance probability (0 <= F <= 1).
`p` | Nonexceedance robability of `z` value.
`para` | The parameters from `lmom2par` or similar.
`z` | Threshold value.
`...` | The additional arguments are passed to the quantile function such as `paracheck = FALSE` for the Generalized Lambda distribution (`quagld`).

### Value

Quantile value for f.

### Author(s)

W.H. Asquith

### See Also

`z.par2cdf`, `par2qua`

### Examples

    # define the real parent (or close)
    the.gpa <- vec2par(c(100,1000,0.1),type='gpa')
    fake.data <- rlmomco(30,the.gpa) # simulate some data
    fake.data <- sort(c(fake.data,0,0,0,0)) # add of zero observations

    # next compute the parameters for the positive data
    gpa <- pargpa(lmoms(fake.data[fake.data > 0]))
    n <- length(fake.data) # sample size
    p <- length(fake.data[fake.data == 0])/n # est. prob of zero value
    F <- nonexceeds() # handy values
    PP <- pp(fake.data) # compute plotting positions of sim. sample

    plot(PP,fake.data,ylim=c(0,5000)) # plot the sample
    lines(F,quagpa(F,the.gpa)) # the parent (without zeros)
    lines(F,z.par2qua(F,p,gpa),lwd=3) # fitted model with zero conditional

    # now repeat the above code over and over again and watch the results

[Package lmomco version 0.92 Index]